Quantifying AI impact in energy transitions: The Energy Justice Impact Assessment (EJIA) framework

2026-09-24

AI Summary

This study addresses the challenge of quantifying justice implications arising from AI applications in energy systems, where existing assessment frameworks lack multidimensional, measurable indicators. To bridge this gap, this work proposes the EJIA framework, which integrates the AI lifecycle with three core principles of energy justice. Employing a PRISMA-guided systematic review, multidimensional matrix mapping, and counterfactual comparative analysis, the framework introduces a novel multidimensional quantitative indicator system stratified by stakeholder groups. By enabling the visualization of differential impacts and cross-group comparability, this approach fills a critical evaluation void at the intersection of AI and energy systems. The effectiveness of the proposed framework is empirically validated through a social housing case study, demonstrating its practical utility for equitable AI deployment in energy contexts.

Abstract

Artificial intelligence is increasingly deployed across energy systems to optimize efficiency, balance supply and demand, and integrate renewable sources. These applications alter how energy systems function, changing how benefits and burdens are distributed, whose needs are represented in system design, and who can influence decision-making, with consequences for energy justice that are rarely quantified. This paper addresses that gap through a systematic review of AI impact assessment approaches, followed by the development of a new framework. Reviewing 26 peer-reviewed frameworks using PRISMA methodology, we find that most address a single impact dimension, only three provide quantifiable indicators, and although 21 reference justice implications, only two operationalize justice-relevant constructs through measurable indicators. No existing framework combines multidimensional impact coverage with quantifiable indicators disaggregated by stakeholder group. We therefore introduce the Energy Justice Impact Assessment (EJIA) framework. Crossing four AI lifecycle stages - data collection, model development, deployment, and continuous adaptation - with the three tenets of energy justice (distributional, recognition, procedural) produces a map of where injustice can arise, which we use to derive a set of environmental, social, and economic outcome indicators. These are measured separately per affected stakeholder group and assessed against a counterfactual across defined phases, enabling observed effects to be attributed to the AI application. The EJIA framework is descriptive rather than normative: it makes differential impacts visible and comparable, while judging whether a pattern constitutes injustice remains a context-dependent decision for practitioners. An exemplary application to an AI-based demand-side response system in social housing demonstrates the framework's use.

Problem

• Artificial Intelligence
• Energy Justice
• Impact Assessment
• Quantification
• Energy Transition

Innovation

• Energy Justice Impact Assessment
• AI lifecycle
• quantifiable indicators
• stakeholder disaggregation
• counterfactual attribution